How To Transcribe Speech To Text
📖 Bu rehber ToolPazar ekibi tarafından hazırlanmıştır. Tüm araçlarımız ücretsiz ve reklamsızdır.
Whisper model tiers
Speech-to-text stopped being a novelty around 2022, when OpenAI’s Whisper model hit roughly 5% word error rate on clean English audio — close to human transcriptionist accuracy. Before Whisper, automated transcription ranged from “usable with heavy cleanup” to “comedic.” Now it’s fast, free on commodity hardware, and good enough for production captions, podcast show notes, and meeting summaries. But it still fails in predictable ways — accents, overlapping speech, noise, punctuation, proper nouns — and choosing the right model and workflow for your use case matters. This guide covers the model tiers, how word error rate is measured, speaker diarization, punctuation insertion, accent handling, noise robustness, and the language-support landscape.
Word Error Rate (WER)
OpenAI’s Whisper is the dominant open model. It ships in sizes:
Speaker diarization
The speed column is relative to real-time: 10x means a 10-minute audio file transcribes in 1 minute. Quality climbs steeply from tiny to medium, then plateaus — large is noticeably better only on difficult audio (accents, noise, music). For clean studio speech, small and medium often produce nearly identical output.
Punctuation insertion
A state-of-the-art Whisper-large model achieves 5–10% WER on clean conversational English. Lower-quality source audio (phone calls, noisy rooms) pushes WER to 15–30%. For non-English and heavy accents, expect 10–40% depending on the target language’s training data volume.
Accent handling
Diarization is the “who spoke when” problem — splitting a transcript into labeled speaker turns. Whisper doesn’t do diarization natively; it transcribes word-by-word with timestamps and leaves speaker attribution to a separate step.
Noisy environments
Common diarization pipelines: pyannote.audio (open source, accurate), AWS Transcribe (cloud, integrated), Deepgram (cloud, fast). They cluster voice embeddings to group similar-sounding segments together, then label them Speaker 1, Speaker 2, etc. Accuracy drops with more speakers and overlapping speech.
Hallucinations
Raw speech recognition produces lowercase, punctuation-free text. A separate punctuation model adds periods, commas, capitalization, and sentence boundaries. Whisper bundles this in; older ASR engines don’t.
Language support
Punctuation is surprisingly hard because speech doesn’t have clear sentence boundaries. Speakers trail off, restart mid-thought, use fillers (“um,” “like,” “you know”). Good punctuation models balance: break sentences where pauses and intonation suggest natural boundaries, but don’t create fragments every time someone takes a breath.
Cloud vs local
Whisper was trained on 680,000 hours of multilingual audio including many accented English variants, which makes it relatively robust. But accuracy still drops for accents underrepresented in the training set. Typical WER penalty:
Timestamps and alignment
Background noise is the #1 WER killer. A clean studio recording might transcribe at 4% WER; the same content with -20dB background chatter can jump to 15% WER. Mitigations:
Common mistakes
Whisper supports ~100 languages, with varying quality. Top-tier (near-English quality): Spanish, French, German, Mandarin Chinese, Japanese, Portuguese, Italian, Korean. Mid-tier (usable, 10–20% WER): Arabic, Hindi, Russian, Indonesian, Turkish, Polish, Dutch. Low-tier (noisy, often 30%+ WER): low-resource languages with limited training data — Swahili, Welsh, Tagalog, Yoruba.
Run the numbers
For non-English, larger models make a bigger difference. Tiny may produce unusable output for French, while medium is excellent.